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Background The increasing rates of clarithromycin (CLR)- and levofloxacin (LVX)-resistant Helicobacter pylori are the main causes of the considerable decrease in the eradication rates of triple therapy and LVX-based regimens. The aims of this study were to determine the rates of CLR- and LVX-resistant H. pylori by the Epsilometer test and to assess the risk factors for this antibiotic resistance among patients with chronic gastritis in the south east area of Vietnam. Methods Gastric biopsy specimens were obtained from 153 patients with H. pylori-positive chronic gastritis for use in culture and in the Epsilometer test to determine CLR and LVX susceptibilities. Results The rates of H. pylori resistance to CLR and LVX were 72.6% and 40.5%, respectively. Dual-resistant H. pylori (to both CLR and LVX) accounted for 30.7% of patients. The rates of high-level resistance to CLR and LVX were 18.9% and 83.9%, respectively. Multivariate analysis showed that age older than 30 years (odds ratio [OR] = 3.80, 95% confidence interval [CI] 1.61–8.97) and history of H. pylori treatment (OR = 8.72, 95% CI 1.90–39.91) were independent risk factors for CLR resistance, whereas only age older than 35 years (OR = 2.42, 95% CI 1.23–4.76) was an independent risk factor for LVX resistance. Conclusions These results revealed high rates of resistance of H. pylori to CLR and LVX in patients with chronic gastritis in the south east area of Vietnam. This suggests that CLR-based triple therapy should not be used for the eradication treatment of H. pylori, and LVX susceptibility testing of H. pylori strains should be performed before choosing alternative regimens.

Mục tiêu: Phát triển mô hình tiên lượng khả năng tìm thấy tinh trùng từ kỹ thuật TESE trên nhóm bệnh nhân vô tinh không bế tắc. Đối tượng và Phương pháp nghiên cứu: Chúng tôi thực hiện một phân tích hồi cứu tại Bệnh viện Mỹ Đức trong khoảng thời gian từ năm 2016 đến năm 2018. Tìm thấy tinh trùng được định nghĩa là có sự hiện diện của ít nhất một tinh trùng di động trong mẫu mô tinh hoàn thu nhận từ kỹ thuật TESE. Các biến số nền và lâm sàng sau đó được đưa vào phân tích hồi quy đơn biến và đa biến để tìm ra các yếu tố tiên lượng độc lập có liên quan đến tỷ lệ tìm thấy tinh trùng. Các yếu tố tiên lượng sau đó được đưa vào mô hình BMA để đưa ra các mô hình tiên lượng khả dĩ. Mô hình tốt nhất là mô hình có số biến số ít nhất, chỉ số BIC thấp nhất, AUC lớn nhất và xác xuất hậu định lớn nhất. Kết quả: Có tổng cộng 125 nam giới vô tinh không bế tắc thực hiện kỹ thuật TESE trong khoảng thời gian nghiên cứu, trong đó có 36 trường hợp (28,8%) tìm thấy tinh trùng. Mô hình tiên lượng SRR tốt nhất bao gồm 2 biến số: tuổi bệnh nhân và nồng độ FSH huyết thanh (xác suất = e[-3,05 – (0,11 x FSH + 0,1 x tuổi)]); BIC: - 486; xác suất hậu định 0,331; AUC = 0,81 (KTC 95% 0,72 - 0,90). Không có sự khác biệt có ý nghĩa thống kê giữa giá trị dự đoán khả năng tìm thấy tinh trùng và kết quả tìm thấy tinh trùng trên thực tế. Kết luận: Mô hình tốt nhất dự đoán khả năng tìm thấy tinh trùng bao gồm có 2 biến số tuổi bệnh nhân và nồng độ FSH huyết thanh. Mô hình này có thể được sử dụng trên lâm sàng để tư vấn cho bệnh nhân trước điều trị.

Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art., Accepted to IEEE Transactions on Multimedia, 2019. arXiv admin note: text overlap with arXiv:1607.05140

This document reports the up-to-date progress towards achieving smart E2E network slicing. It focuses on the prototyping of enabling technologies for E2E network slicing across network segments, their intent-based Management and Orchestration, and AI-based RAN resource allocation enabled with the Cognitive Plane’s Multi-Agent Deep Reinforcement Learning platform. Moreover, it covers preliminary integration work between different network slicing enablers over the E2E data path, with the MANO plane, and with the MA-DRL platform. Together, these technological enablers, algorithmic advances and initial integration work lay down the foundation for implementing the targeting E2E slicing system.

We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D-3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows and balconies) in urban localization. Extensive experiments demonstrate that our 2D-3D correspondence search achieves the state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google street view image dataset show the potential of large-scale localization entirely on a typical mobile device.

This document reports all the activities and outcomes related to the definition of the network requirements, key function blocks and system architecture for the 6G BRAINS project. Based on a methodology informed by use case requirements etc., the deliverable presents an overall logical architecture with key function blocks defined and organised into operational planes. Based on the logical architecture, detailed functional architectures are defined to address the major technical challenges in the project.

This paper proposes a decentralized capacitor voltage control strategy for the flying capacitor multilevel inverter (FC-MLI) in combination with the decentralized self-aligned phase-shifted pulse width modulation (PWM) (self-aligned carriers) method. For FC-MLI, the capacitor voltage of switching modules requires a special condition of initial capacitor voltage to startup and reach the desired value. To overcome this issue, a decentralized capacitor voltage control algorithm is proposed to regulate the capacitor voltage to the required value under normal operation as well as in case of reconfiguration of the system when the number of switching cells is changed. The key approach is that each switching cell controller of the FC-MLI synchronizes and regulates its own voltage effectively by using information from its neighboring cell controllers. The validity of proposed decentralized capacitor voltage control method in association with the self-aligned phase-shifted carriers is shown by simulation results using Matlab Simulink software.

Đặt vấn đề: Điều kiện làm việc căng thẳng có thể ảnh hưởng xấu lên sức khoẻ và tinh thần của nhân viên y tế cũng như kết quả điều trị bệnh nhân hiếm muộn. Mục tiêu nghiên cứu là đánh giá tỉ lệ hiện mắc và bản chất của các yếu tố stress trên nhân viên y tế đang công tác tại các trung tâm IVF. Phương pháp: Điều tra cắt ngang. Tất cả nhân viên y tế tại 7 đơn vị IVF miền Nam Việt Nam được mời tham dự. Thông tin sẽ được thu thập trực tiếp thông qua bảng thu thập chỉ số stress nghề nghiệp (OSI). Kết quả: Có 35 y tá, 19 bác sỹ và 51 nhân viên phòng lab tham gia. Trong đó, nhóm y tá có điểm số stress nghề nghiệp cao nhất. Điểm số này có mối liên hệ chặt chẽ với “yêu cầu công việc cao” (p

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks., Accepted to IEEE ICIP 2018

We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g. Binary Autoencoder (BA) [1], Iterative Quantization (ITQ) [2], in standard evaluation metrics for the three main benchmark datasets.

In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subjected to a hashing function that produces a binary hash code. In previous work, the aggregating and the hashing processes are designed independently. In this paper, we propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization produces aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, we also propose a fast version of the recently-proposed Binary Autoencoder to be used in our proposed framework. We perform extensive retrieval experiments on several benchmark datasets with both SIFT and convolutional features. Our results suggest that the proposed framework achieves significant improvements over the state of the art.

Background: Regimens with Clarithromycin-based and Levofloxacin-based triple were standard treatments for H.pylori eradication, but the increased prevalence of H.pylori strains resistant to Clarithromycin (CLA) and Levofloxacin (LEV) had became the main factor for treatment failure. Aims: to determine the rate of H. pylori isolates in culture and antibiogram using Epsilometer (Etest) with CLA and LEV done successfully in patients who have positive CLO test; to determine the prevalence of primary and secondary resistance of H. pylori to CLA and LEV. Materials and methods: 60 patients who presenting with gastroduodenal diseases at Thong Nhat-Dong Nai Hospital, from 11/2013 to 12/2013, with CLO test positive, were recruited to the study. H.pylori isolates were cultured with PYL medium, then antibiogram against CLA and LEV done by Epsilometer test (Etest) method with Muller Hinton Agar. Results: the rate of successful culture of H.pylori isolates was 91.7%; the mean time for H.pylori growth was 4.0±1.2 days (3-10 days), of these, 81.8% growing in 3-4 days; the successful rate of antibiograms was 89,1%; the rate of primary and secondary resistance to CLA and LEV were 64.1-100% and 29.2-100% respectively; sensitivity rate and resistance rate to both kinds of antibiotics were 20.4% and 22.4%. Conclusions: We should not choose the CLA-based and LEV-based triple therapies in treatment for patients infected with H.pylori in Dong Nai province, except when having sensitive evidence; rather, we should choose Bismuth-based quadruple therapy or use CLA with a different usage such as sequential or concomitant therapy. Key words: H.pylori, antibiotic resistance, primary culture, antibiogram

Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy., Comment: Accepted to ACM MM 2017